Last updated: 2026-08-14
In a verification test, Model ML completed a Word and PowerPoint deliverable in under 3 minutes, versus over an hour for McKinsey and Bain consultants, while catching more errors. The London fintech builds client-ready Word, PowerPoint, and Excel outputs from a firm's own data, with a source citation for every figure it produces.
About Model ML
Model ML is an AI workspace built for financial services firms, founded in London by brothers Chaz and Arnie Englander, repeat entrepreneurs behind Fat Llama (acquired by Hygglo) and Fancy (acquired by Gopuff). The company now runs a team of around 80 people across London, New York, Singapore, and Hong Kong, serving investment banks, private equity and credit shops, and asset and wealth managers who need client-ready output without the manual grunt work of assembling it by hand. The platform works by connecting to a firm's own systems, Salesforce, Google Workspace, internal data rooms, and turning that data into structured deliverables through five linked tools: Workflows for end-to-end document generation, Grids for large-scale prompt-driven analysis, Document Review for extracting figures from data rooms, Chat for follow-up questions, and Notetaker for capturing meeting context. Each number the system produces is traceable to its origin document via a superscript link, and it is built to abstain rather than guess when a supporting answer is missing, which matters in a regulated industry where an unexplainable figure is a liability. Model ML is best suited to bankers building pitch decks, credit and PE analysts working through virtual data rooms, and asset managers who need recurring tearsheets or portfolio reports in their firm's existing Word, PowerPoint, or Excel templates rather than a generic export. It is not aimed at solo users or small non-finance teams; the workflows assume institutional data sources and an internal IT and security review before rollout. There is no published pricing. Model ML sells through custom annual enterprise contracts arranged after a demo at modelml.com, with deployment options that include a shared cloud environment or a fully siloed, self-hosted instance for firms that require data isolation. The product is web-based, with SSO via SAML or OIDC and passwordless email login by default. The company's Series A closed in November 2025, backed in part by QED, 13Books, Latitude, Y Combinator, and HSBC Asset Management's venture arm, on top of a smaller seed round Latitude and LocalGlobe led the previous year. Model ML now runs production document generation on OpenAI's GPT-5.6 Sol model.
Pricing
No public pricing tiers. Every account is a negotiated annual contract sized to seat count and workflow scope, quoted only after a sales demo at modelml.com. There is no self-serve signup or free trial.
Key Features
- Workflows: Turns a data room or internal dataset into a structured, cited financial deliverable, rendering the output as an editable Word, PowerPoint, or Excel file in the client's existing template.
- Grids: Runs thousands of prompts across a dataset inside a reusable analysis template, producing institutional-scale comparisons instead of one-off spreadsheet pulls.
- Document Review: Processes virtual data rooms with more than 100,000 rows across hundreds of files in a single pass, extracting figures with footnote citations back to the source document.
- Chat: Lets analysts ask follow-up questions about any Workflow or Grid output in natural language instead of re-running a manual search.
- Notetaker: Captures meeting and task context automatically so it can be pulled into a later Workflow or Grid output without manual re-entry.
- Agentic Dashboards: Builds real-time, interactive reporting dashboards that update automatically as underlying data changes, instead of static end-of-day exports.
- Source verification: Every generated figure carries a clickable footnote back to its source document, and the system abstains instead of guessing when it cannot find a supporting answer.
- Enterprise access controls: Supports SAML/OIDC single sign-on with just-in-time provisioning and passwordless email login by default, plus an optional fully siloed or self-hosted deployment.
Pros
- On Model ML's own evaluation, its production model completed a PowerPoint benchmark in 100% of test runs and cleared the firm's professional-readiness bar 43.3% of the time, versus 76% completion and a 26.7% readiness rate for a rival frontier model.
- At one global asset manager, a bespoke tearsheet that took an analyst about an hour to assemble now takes roughly 5 minutes with Model ML.
- Backed by $87.5M in total funding, including a $75M Series A led by FT Partners in November 2025 with participation from HSBC Asset Management's venture arm.
Cons
- Pricing is entirely custom and unpublished; buyers must go through a sales demo before learning what a contract costs, making upfront comparison against listed-price competitors hard.
- Built specifically for large financial institutions, so solo analysts, small funds, or non-finance teams get little value from the platform.
- Production workflows depend on third-party models such as GPT-5.6 Sol, so output quality and cost can shift when the underlying model provider changes pricing or behavior.
- No public G2, Trustpilot, or Product Hunt rating was found as of writing, so buyers have only vendor-published benchmarks and case studies to evaluate the product against.
Frequently Asked Questions
How much does Model ML cost in 2026?
Model ML does not publish subscription tiers or per-seat prices. It sells to financial institutions through a custom annual enterprise contract that you get by booking a demo at modelml.com, with the price scoped to seat count, workflow coverage, and deployment type.
Is Model ML free to use?
No. Model ML has no free tier and no self-serve signup; every account starts with a sales conversation and a live demo. There is no published trial period, so evaluating the platform means requesting access directly from the company.
What are the best alternatives to Model ML?
Rogo focuses on investment-banking deliverables such as pitch memos and deal prep, Hebbia specializes in large-scale document analysis for due diligence, and ProSights targets private equity portfolio reporting. Choose Rogo for banker-style workflows, Hebbia for document search at scale, or ProSights if ongoing PE portfolio tracking is the priority.
How does Model ML compare to Rogo in 2026?
Rogo is built for investment-banking deliverables like pitch memos and deal prep, while Model ML spans credit, private equity, and asset-management reporting as well as banking, generating client-ready Word, PowerPoint, and Excel outputs with source citations. Pick Rogo if banking research is the main job; pick Model ML if the workflow spans credit or asset-management reporting too.
How do you get started with Model ML?
Book a demo through modelml.com, where the team scopes which workflows you need, pitch decks, credit memos, tearsheets, or data-room diligence, and configures SAML or OIDC single sign-on for your organization. Rollouts also cover deployment choice and a security review, since customers are typically banks, credit funds, and portfolio managers with strict IT requirements.
Top Alternatives
- Harvey: Pick Harvey if your workspace needs to serve legal teams; pick Model ML if your deliverables are banking, credit, or asset-management documents.
- Glean: Pick Glean if you need enterprise-wide knowledge search across every department; pick Model ML if you need finance-specific Word, PowerPoint, and Excel output with cited data-room analysis.
- Microsoft Copilot: Pick Microsoft Copilot if you want a general Office assistant already bundled into your M365 license; pick Model ML if you need a finance-native platform that outputs audit-ready decks and models.
- ChatGPT: Pick ChatGPT if you need a general-purpose assistant for any task; pick Model ML if you need a finance workspace built for data-room-scale document processing and audit trails.